Predictive Dynamic Scheduling for Smart Factory Optimization
Manufacturing - Aerospace
Develop a smart factory solution to improve maintenance planning in industrial applications
Created a dynamic scheduling dashboard that combines both MES and Predictive Maintenance (PdM) data to optimize maintenance scheduling, reduce unplanned downtime, improve equipment availability and decrease waste and costs
In manufacturing, production information from manufacturing execution systems (MES) and machine health are often looked at separately. This approach can prevent companies from taking full advantage of the actionable intelligence that can be derived from correlating these two key sources of information to improve maintenance decision-making. Through a funding program offerred by MxD, Predictronics partnered with Lockheed Martin, Northeastern University and FORCAM to create an integrated framework that merges machine health and dynamic scheduling in order to enhance ROI. This integration was developed to show manufacturing engineers, academics, and university students how to improve maintenance practices and enhance business by employing predictive dynamic scheduling.
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Predictronics worked with our partners to merge MES data and PdM data into one unified dashboard and provide predictive scheduling with health monitoring capabilities. The project established a two-pronged approach, first creating an analytics-based computing engine to derive equipment health condition and predict remaining useful life and then integrating MES information with machine health information to support optimal decision-making for maintenance and production scheduling.
Companies with time-based preventive maintenance policies often conduct insufficient or excessive maintenance due to a lack of meaningful equipment health data in a dynamic manufacturing environment. With the newly developed unified dashboard, businesses can continuously monitor critical machine components, visualize the machine health condition, predict failures, and dynamically schedule preventive maintenance in order to optimize production. This solution can also be customized based upon the user’s maintenance criteria (e.g. spare part lead time, downtime costs, inventory costs, and more), preventing wasted spare parts, resources, and labor costs and decreasing overall inventory overhead and capital expenditures.. The predictive dynamic scheduling dashboard would ideally target companies in the automotive and aerospace industries and is best suited for manufacturing equipment such as CNC machines, industrial robots and stamping press machines.